Find Interview Questions for Leidos

Right Answer:
**Benefits:** Faster time to market, reduced risk, improved quality, faster feedback, happier teams.

**Challenges:** Requires high automation, strong collaboration, cultural shift, investment in infrastructure, and robust testing.

Right Answer:
I would assess the organization's context, choose a suitable scaling framework like SAFe or LeSS based on needs, implement it incrementally with training and coaching, focusing on value delivery, and continuously inspect and adapt the implementation based on feedback and results.

Right Answer:
* **Listen actively:** Understand their concerns and perspective.
* **Communicate clearly and frequently:** Keep them informed about progress and challenges.
* **Find common ground:** Focus on shared goals and objectives.
* **Be transparent:** Share data and evidence to support decisions.
* **Facilitate collaboration:** Encourage open dialogue and problem-solving.
* **Coach and mentor:** Help team members grow and improve.
* **Escalate when necessary:** Involve a Scrum Master or manager if the situation doesn't improve.

Right Answer:
We prioritize features or tasks in an Agile sprint using a combination of factors like business value, risk, effort/size, dependencies, and urgency. Product Owner usually leads this, using techniques like MoSCoW (Must have, Should have, Could have, Won't have) or story pointing, to ensure the most valuable items are tackled first.

Right Answer:
To facilitate effective sprint retrospectives, I would:

1. **Set the Stage:** Create a safe and open environment where the team feels comfortable sharing.
2. **Gather Data:** Collect information about what went well, what didn't, and any challenges faced during the sprint.
3. **Generate Insights:** Facilitate a discussion to identify root causes and patterns.
4. **Decide on Actions:** Collaborate to define specific, actionable, measurable, achievable, relevant, and time-bound (SMART) improvements.
5. **Close the Retrospective:** Summarize action items and assign owners.
6. **Follow Up:** Track progress on action items in subsequent sprints to ensure continuous improvement.

Right Answer:
Synchronous API calls wait for the response before moving on to the next task, while asynchronous API calls allow the program to continue executing other tasks while waiting for the response.

Right Answer:
The different types of APIs are:

1. **Open APIs (Public APIs)** - Available to developers and third parties.
2. **Internal APIs (Private APIs)** - Used within an organization.
3. **Partner APIs** - Shared with specific business partners.
4. **Composite APIs** - Combine multiple endpoints into a single call.
5. **Web APIs** - Accessible over the internet using HTTP/HTTPS.

Right Answer:
CORS, or Cross-Origin Resource Sharing, is a security feature implemented by web browsers that allows or restricts web applications from making requests to a domain different from the one that served the web page. It affects API development by requiring developers to configure their APIs to specify which origins are allowed to access their resources, ensuring that only trusted domains can interact with the API.

Right Answer:
REST (Representational State Transfer) is an architectural style that uses standard HTTP methods and is typically more lightweight and easier to use, while SOAP (Simple Object Access Protocol) is a protocol that relies on XML for message format and has strict standards for security and transactions. REST is generally more flexible and faster, while SOAP is more suited for enterprise-level services requiring high security and reliability.

Right Answer:
The common status codes in HTTP responses are:

- **200**: OK
- **201**: Created
- **204**: No Content
- **400**: Bad Request
- **401**: Unauthorized
- **403**: Forbidden
- **404**: Not Found
- **500**: Internal Server Error
- **502**: Bad Gateway
- **503**: Service Unavailable

Right Answer:
Correlation is a statistical measure that indicates the extent to which two variables fluctuate together, while causation implies that one variable directly affects or causes a change in another variable.

Right Answer:
The different types of data analysis are:

1. Descriptive Analysis
2. Diagnostic Analysis
3. Predictive Analysis
4. Prescriptive Analysis
5. Exploratory Analysis

Right Answer:
The purpose of feature engineering in data analysis is to create, modify, or select variables (features) that improve the performance of machine learning models by making the data more relevant and informative for the analysis.

Right Answer:
Exploratory Data Analysis (EDA) is the process of analyzing and summarizing datasets to understand their main characteristics, often using visual methods. It helps identify patterns, trends, and anomalies in the data before applying formal modeling techniques.

Right Answer:
Supervised learning uses labeled data to train models, meaning the output is known, while unsupervised learning uses unlabeled data, where the model tries to find patterns or groupings without predefined outcomes.

Right Answer:

Analyzing data and drawing conclusions is all about turning raw numbers into useful insights. Whether you’re working with survey results, sales figures, or performance metrics, the process follows a few key steps to help you make sense of the data and use it for decision-making.

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🔍 Key Steps to Analyze and Interpret Data:

1. Understand the Goal
 Start by asking: What question am I trying to answer?
 Having a clear objective keeps your analysis focused and relevant.

2. Collect and Organize the Data
 Make sure your data is complete, accurate, and well-organized.
 Group it by categories, time periods, or other relevant factors.

3. Clean the Data
 Remove duplicates, fix errors, and fill in missing values.
 Clean data ensures that your results are trustworthy.

4. Explore and Visualize
 Use charts, graphs, or summary statistics to explore patterns and trends.
 This helps you spot outliers, relationships, or shifts in behavior.

5. Compare and Segment
 Look at differences between groups, time periods, or categories.
 Ask: What’s changing? What stands out?

6. Apply Statistical Methods (if needed)
 Use averages, percentages, correlations, or regression analysis to go deeper and support your observations with evidence.

7. Draw Conclusions
 Based on your findings, answer the original question.
 What does the data reveal? What decisions or actions does it support?

8. Communicate Clearly
 Summarize your results in simple, clear language — supported by visuals and examples when needed.

Right Answer:

Regression analysis is a statistical method used to understand the relationship between one dependent variable and one or more independent variables. In simpler terms, it helps you see how changes in one thing affect another.

For example, you might use regression to see how advertising budget (independent variable) affects product sales (dependent variable).

Right Answer:

Trends and patterns in data help you see the bigger picture. They show how values change over time, how different variables are connected, and what behaviors or outcomes are repeating. Spotting trends and patterns makes raw numbers meaningful — and helps you make smarter decisions.

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🔍 Why Trends and Patterns Matter in Data Interpretation:

1. Reveal What’s Changing
Trends show the direction of data over time — whether it’s going up, down, or staying stable.
✅ Example: An increasing sales trend signals business growth.

2. Help Predict Future Outcomes
If a pattern keeps repeating, you can often use it to forecast what’s likely to happen next.
✅ Example: If customer visits always drop in August, you can plan ahead.

3. Identify Relationships
Patterns show how two variables may be connected.
✅ Example: If higher website traffic always leads to more sales, you’ve found a useful link.

4. Spot Problems or Opportunities
Unexpected changes or breaks in a trend can signal issues — or reveal new chances for improvement.
✅ Example: A sudden drop in customer satisfaction may alert you to a service issue.

5. Support Data-Driven Decisions
Trends and patterns turn raw data into actionable insights, helping teams make informed choices backed by evidence.

Right Answer:

Data interpretation is the process of reviewing, analyzing, and making sense of data in order to extract useful insights and meaning. It involves understanding what the data is telling you — beyond just the numbers — so you can make informed decisions, spot patterns, and solve problems.

It’s not just about collecting data; it’s about understanding what that data means.

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🔍 Why Is Data Interpretation Important?

1. Turns Raw Data into Insights
Without interpretation, data is just numbers. Interpreting it reveals trends, relationships, and key findings.

2. Supports Better Decision-Making
Good interpretation helps individuals, businesses, and organizations make smart, evidence-based decisions.

3. Identifies Patterns and Problems
It helps you understand what’s working, what’s not, and what needs improvement.

4. Improves Communication
Clear interpretation makes it easier to explain data to others — whether in reports, presentations, or discussions.

5. Drives Strategy and Planning
Whether you’re running a business, doing research, or managing a project — interpreting data helps you plan for the future based on facts.

Right Answer:

Incomplete or missing data is a common challenge in data analysis. Whether it’s skipped survey responses, blank spreadsheet cells, or unavailable values, missing data can affect the accuracy and reliability of your results.

The key is to handle missing data thoughtfully so you can still draw valid conclusions without misleading your interpretation.

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🔍 Common Ways to Handle Missing Data:

1. Identify the Missing Data
 Start by locating where and how much data is missing.
 Check: Is it random or following a pattern? Are entire sections missing or just a few values?

2. Remove Incomplete Entries (if appropriate)
 If only a small number of rows are missing data, and they don’t heavily impact the dataset, you can safely remove them.

3. Use Imputation (Estimate Missing Values)
 If the dataset is large and important, you can fill in missing values using methods like:
– Mean or median substitution (for numerical data)
– Mode (for categorical data)
– Regression or predictive models (for more advanced cases)

4. Use Available Data Only
 In some cases, you can perform analysis using just the complete parts of the dataset — as long as it doesn’t bias your results.

5. Flag and Acknowledge Missing Data
 Be transparent in reports. Clearly mention how much data is missing and how it was handled.

6. Ask Why the Data Is Missing
 Sometimes missing data reveals a deeper issue (e.g., system errors, survey confusion). Understanding the cause can help prevent future problems.